1. Data Assimilation and Predictability Studies for Improving TC Intensity Forecasts PI: Takemasa Miyoshi University of Maryland, College Park miyoshi@atmos.umd.edu Co-PIs: E. Kalnay and K. Ide (UMD) and C. Bishop (NRL) Co-Is: T. Enomoto and N. Komori (Japan), S.-C. Yang (Taiwan), H. Li (China) Collaborators: T. Nakazawa (WMO), P. Black (NRL) Project Researcher: M. Kunii (UMD) February 2011, NOPP TC Topic Review, Miami

2. Adaptive inflation/localization Ensemble sensitivity methods Lagrangian data assimilation Better use of observations Project Overview CFES-LETKF using the Earth Simulator Coupled Atmos-Ocean error covariance Large-scale environment DA Method LETKF Local Ensemble Transform Kalman Filter (Hunt et al. 2007) WRF-LETKF Cloud-resolving data assimilation Mesoscale

3. Achievements Studies on methods –Adaptive inflation method was developed. –Impact of resolution degradation was investigated. CFES-LETKF –AFES-LETKF experiments were performed. WRF-LETKF –WRF-LETKF system was developed and tested. –Impact of SST perturbations was examined. –Observation impacts were computed for T-PARC dropsonde observations.

4. Deliverables Peer-reviewed publications –3 accepted Miyoshi and Kunii (2011, Pure Appl. Geophys.) on the WRF-LETKF Miyoshi (2011, MWR) on the adaptive inflation method Miyoshi et al. (2010, WAF) on the resolution degradation of the initial condition –1 submitted Miyoshi et al. on the observation error correlations Conference presentations –9 orals (2 invited), 2 posters

5. ADAPTIVE INFLATION Studies on methods:

6. Variance underestimation T=t0T=t1 An initial condition with errors FCST Ens. mean P Forecast ensemble tends to be under-dispersive.

7. Covariance inflation T=t0T=t1 An initial condition with errors FCST Ens. mean P  (1+a)P Covariance inflation inflates the underestimated variance.

8. Adaptive inflation Posterior PriorObs Li et al. (2009) applied the Gaussian assumption. PosteriorPriorObs Non-Gaussianity is very weak.

9. Localization of inflation estimates Each grid point is treated independently. Local Ensemble Transform Kalman Filter (LETKF, Hunt et al. 2007) Apply the maximum likelihood estimate at each grid point. PosteriorPriorObs

10. Test with a T30/L7 AGCM

11. Adaptive inflation estimates

12. Impact of adaptive inflation ~20% improvements of analysis RMSE Better match between RMSE and spread

13. AFES-LETKF CFES-LETKF:

14. Experimental settings AFES-LETKF settings ResolutionT119/L48 Ensemble size63+1 Covariance inflationFixed 10% Covariance localization400 km, 0.4 ln p 4D-LETKF

15. Surface SondesAircraft Ships & buoys QuikScatAMV

16. Analysis of TY Sinlaku (2008) Best track AFES-LETKF

17. Analysis and ensemble spread

18. REAL OBSERVATIONS WRF-LETKF:

19. Experimental settings LETKF settings WRF settings Ensemble size20 Lateral boundary conditionsUnperturbed Covariance inflationAdaptive (Miyoshi 2010) Fixed 20% (smaller above level 20) Covariance localization400 km, 0.4 ln p Analyzed variablesu, v, w, T, ph, qv, qc, qr Domain size136(137) x 108(109) x 39(40) Horizontal grid spacing~ 60 km WRF versionWRF-ARW 3.2

20. 6-hr Forecast at 12Z 12 Sep. 2008 NCEPGSMAP NOOBSLETKF after 9 days cycle

21. Adaptive inflation Lower troposphereMiddle troposphere ● ADPSFC ▲ SFCSHP □ ADPUPA ◇ AIRCFT ○ PROFLR × SATWND ■ VADWND △ SPSSMI ＋ QKSWND  Adaptive inflation accounts for imperfections such as model errors and limited ensemble size.  Large adaptive inflation > 100 % (2.0) appears occasionally and is appropriate in the limited regions.

22. Ensemble spread (T500) ADAPTIVEFIXED20% NOOBS Too small (large) over the densely (sparsely) observed areas Unperturbed BC is limited near the boundaries

23. 6-hr forecast vs. radiosondes  Adaptive inflation performs well.

24. Computational time per 6-h cycle Computing environment: Number of nodes: 7 (Linux servers) CPUs per node: 2 x Quad-Core AMD Opteron 2382 (2.6 GHz) Total number of cores: 7 x 8 = 56 # mem.cores per fcst. mem. mem. per node total cores for fcst. cores for LETKF 20 +1 * 2342 27 +1 * 245642 34 +1 * 153542 41 +1 * 1642 * +1: forecast from ensemble mean analysis

25. Impact of SST perturbations  Consistently better with SST perturbations.

26. OBSERVATION IMPACT WRF-LETKF:

27. Introduction Traditionally, observation impact has been estimated by carrying out the data denial experiment (expensive). Methods without data denial experiment: –Adjoint-based method (Langland and Baker 2004) –Ensemble-based method (Liu and Kalnay 2008) In this study –Estimate impact of real observational data with the WRF-LETKF system using the ensemble-based method.

28. Experimental settings TY0813 (SINLAKU) Track MSLP OBSIMP settings Verification time6 h, 12 h NormKE Targeted regionNONE, 5 × 5° around the TC

29. Observation impact for each type Forecast error reduction (J/kg, KE) Observation count Observation impact for each observation type (except for satellite radiances) (9/8 12 UTC – 9/12 12UTC, NW Pacific)

30. Observation Impact 00UTC Sep. 11 2008 Forecast error reduction (J/kg, KE) SONDE AMVSPSSMI

31. Targeted Norm No targeting Targeting (5 × 5° around TC ) 00UTC Sep. 11 2008 Forecast error reduction (J/kg, KE) in the targeted area ~1460 km Radius of influence

32. Impact of DOTSTAR dropsondes 6 h fcst error reduction12 h fcst error reduction J/kg

33. Denying negative impact data 6 h fcst error reduction12 h fcst error reduction J/kg

34. Denying negative impact data improves forecast! 6hr fcst error is reduced Estimated observation impact 6-h track forecast is actually improved!! 1.5-day forecasts

35. Denying negative impact data improves forecast! 12hr fcst error is reduced Estimated observation impact 12-h track forecast is actually improved!! 1.5-day forecasts

36. Impact of NRL P-3 dropsondes

37. Impact of WC-130J dropsondes

38. Impact of DLR Falcon dropsondes

39. FUTURE PLANS

40. Plans More focusing on TC intensity forecasting –Investigating air-sea coupled covariance around TC –Higher-resolution experiments to resolve TC structures –Predictability studies by ensemble prediction –More TCS-08 case studies More analysis of the impact of aircraft observations –Impacts of lower-level and upper-level flights at each stage Use of rapid-scan cloud images Direct assimilation of best-track data –Possibly, further ITOP-10 case studies Cases with joint DOTSTAR and WC-130J missions

41. The LETKF code is available at: http://code.google.com/p/miyoshi/

42. RESOLUTION DEGRADATION Studies on methods:

43. Experience from operations at JMA T959 operational forecasts T319 forecasts from T959 analysis (operational Typhoon EPS) T319 forecasts from T319 analysis Simply eliminating higher wavenumber components has negative impact on TC track forecasts.

44. Methods to initialize T319 1.Smooth spectral truncation 2.One-time low-resolution DA Operational Typhoon EPS Test1: Smooth truncation Test2: One-time DA

45. Results of Typhoon Nuri (2008) Best track observation T959 operational forecast T319 forecasts (operational EPS) T319 forecasts from T319 analysis Test1: Smooth truncation Test2: One-time DA

46. Near-center TC structure Bad T319 forecast Better T319 forecast Operational T959 Conclusion: It is important to consider the model’s resolving capability in data assimilation T319 analysis